Can RTX 3070 Ti run Wan 2.2 TI2V 5B?
Yes — runs locally
~40 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 3070 Ti (8 GB VRAM) handles Wan 2.2 TI2V 5B comfortably using the Q8 quantization, which fits in 10.0 GB. Expected throughput is around 40 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. in interactive use. Open-weights text-to-video and image-to-video model. Generates 5-second 480p clips on a single 24 GB card. The current open-source video sweet spot.
Setup tutorial: Wan 2.2 TI2V 5B on RTX 3070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
The Wan 2.2 TI2V 5B model runs on an NVIDIA GeForce RTX 3070 Ti with a Grade C performance, using the Q8 quantization. Expect ~36 tok/sec with snappy responsiveness.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows 10/11 or Linux), and the latest NVIDIA drivers (version 470.82.01 or later) installed. Additionally, you need CUDA 11.4 or later.
Expected performance
With the Q8 quantization, expect a token generation rate of ~36 tok/sec and 10.0GB VRAM in use, leaving about 2GB of VRAM for context. This allows for a practical context window of several hundred tokens, suitable for generating 5-second 480p video clips.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q8 quantized model (5.0GB file) from Hugging Face.
ollama pull Wan-AI/Wan2.2-TI2V-5B-Q83. Run it
ollama run Wan2.2-TI2V-5B-Q8 --n-gpu-layers 16 --flash-attn
ollama interactive Wan2.2-TI2V-5B-Q84. Optimize for RTX 3070 Ti
For optimal performance on the NVIDIA GeForce RTX 3070 Ti with 8GB VRAM, set `--n-gpu-layers` to 16 to maximize the number of layers offloaded to the GPU. Enable `--flash-attn` to reduce memory usage and improve speed. Given the 8GB VRAM, you will have approximately 2GB of headroom for context, which is sufficient for most text-to-video tasks.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers with `--n-gpu-layers 8` or enable `--cpu-offload` to offload more computation to the CPU.
Slow token generation rate
Ensure that `--flash-attn` is enabled and try increasing the batch size with `--batch-size 16`.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Re-run the download command: `ollama pull Wan-AI/Wan2.2-TI2V-5B-Q8`.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. Use LM Studio for a more user-friendly interface, llama.cpp for better performance on CPUs, and Jan for distributed training setups. For the NVIDIA GeForce RTX 3070 Ti, Ollama provides a good balance of ease of use and performance.
Other models that run great on RTX 3070 Ti
FAQ (20)
What GPU do I need to run Wan 2.2 TI2V 5B?
To run Wan 2.2 TI2V 5B, you need a GPU with at least 10 GB of VRAM. For optimal performance, a GPU with 16 GB or more is recommended.
Is Wan 2.2 TI2V 5B good for coding?
Wan 2.2 TI2V 5B is primarily designed for generating video content, not for coding tasks. It may not be suitable for code generation or programming assistance.
Wan 2.2 TI2V 5B vs Llama 3.1 8B?
Wan 2.2 TI2V 5B is a 5B parameter model focused on video generation, while Llama 3.1 8B is a larger language model with 8B parameters, better suited for text-based tasks.
Can I run Wan 2.2 TI2V 5B on a Mac?
Yes, you can run Wan 2.2 TI2V 5B on a Mac as long as your Mac has a compatible GPU with at least 10 GB of VRAM.
How much VRAM does Wan 2.2 TI2V 5B need?
Wan 2.2 TI2V 5B requires between 10.0 GB and 16.0 GB of VRAM, depending on the quantization level used.
Is Wan 2.2 TI2V 5B censored?
Wan 2.2 TI2V 5B is not inherently censored, but it may include content filters to prevent the generation of inappropriate content.
Is Wan 2.2 TI2V 5B commercial-use allowed?
Yes, Wan 2.2 TI2V 5B is licensed under Apache-2.0, which allows for commercial use without additional fees.
Wan 2.2 TI2V 5B context length?
The context length for Wan 2.2 TI2V 5B is currently unknown, as it is not specified in the model documentation.
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